A framework for significance analysis of gene expression data using dimension reduction methods.

ABSTRACT:

Background

The most popular methods for significance analysis on microarray data are well suited to find genes differentially expressed across predefined categories. However, identification of features that correlate with continuous dependent variables is more difficult using these methods, and long lists of significant genes returned are not easily probed for co-regulations and dependencies. Dimension reduction methods are much used in the microarray literature for classification or for obtaining low-dimensional representations of data sets. These methods have an additional interpretation strength that is often not fully exploited when expression data are analysed. In addition, significance analysis may be performed directly on the model parameters to find genes that are important

SUBMITTER: Gidskehaug L 

PROVIDER: S-EPMC2194745 | biostudies-literature | 2007 Sep

REPOSITORIES: biostudies-literature